Semantic segmentation method for myocardial contrast echocardiogram based on DeepLabV3+ deep learning architecture.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiology

Background:

• Myocardial contrast echocardiography (MCE) is a non-invasive technique for assessing myocardial perfusion to detect coronary artery disease.
• Accurate myocardium segmentation in MCE frames is crucial for automated perfusion quantification but is challenging due to low image quality and complex cardiac structures.

Purpose of the Study:

• To propose a deep learning semantic segmentation method for improved myocardium segmentation in MCE.
• To enhance the detection of coronary artery disease through more precise MCE perfusion quantification.

Main Methods:

• A modified DeepLabV3+ architecture incorporating atrous convolution and atrous spatial pyramid pooling was developed.
• The model was trained and tested on MCE sequences from 100 patients across three chamber views (apical two-chamber, three-chamber, and four-chamber).
• Performance was evaluated using Dice coefficient and Intersection over Union metrics, comparing against established methods like PSPnet and U-net.

Main Results:

• The proposed method achieved high performance with Dice coefficients of 0.84, 0.84, and 0.86 for the three chamber views.
• Intersection over Union scores were 0.74, 0.72, and 0.75, demonstrating superior segmentation accuracy.
• The method outperformed existing state-of-the-art techniques, including original DeepLabV3+, PSPnet, and U-net.

Conclusions:

• The developed deep learning model significantly improves myocardium segmentation accuracy in MCE.
• This advancement holds potential for more reliable non-invasive detection of coronary artery disease.
• Analysis of model complexity versus performance indicates practical feasibility for clinical application.